diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index f9fefe17..d2cc3e41 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -771,6 +771,50 @@ pub struct CudaStorage {
     device: CudaDevice,
 }
 
+pub trait CudaDType: Sized {
+    fn as_cuda_slice(s: &CudaStorage) -> Result<&CudaSlice<Self>>;
+    fn wrap_cuda_slice(s: CudaSlice<Self>, dev: CudaDevice) -> CudaStorage;
+}
+
+macro_rules! cuda_dtype {
+    ($ty:ty, $dtype:ident) => {
+        impl CudaDType for $ty {
+            fn as_cuda_slice(s: &CudaStorage) -> Result<&CudaSlice<Self>> {
+                match &s.slice {
+                    CudaStorageSlice::$dtype(data) => Ok(&data),
+                    _ => Err(crate::Error::UnexpectedDType {
+                        expected: DType::$dtype,
+                        got: s.dtype(),
+                        msg: "unexpected dtype",
+                    }
+                    .bt()),
+                }
+            }
+
+            fn wrap_cuda_slice(slice: CudaSlice<Self>, device: CudaDevice) -> CudaStorage {
+                let slice = CudaStorageSlice::$dtype(slice);
+                CudaStorage { slice, device }
+            }
+        }
+    };
+}
+cuda_dtype!(u8, U8);
+cuda_dtype!(u32, U32);
+cuda_dtype!(f16, F16);
+cuda_dtype!(bf16, BF16);
+cuda_dtype!(f32, F32);
+cuda_dtype!(f64, F64);
+
+impl CudaStorage {
+    pub fn wrap_cuda_slice<T: CudaDType>(slice: CudaSlice<T>, device: CudaDevice) -> CudaStorage {
+        T::wrap_cuda_slice(slice, device)
+    }
+
+    pub fn as_cuda_slice<T: CudaDType>(&self) -> Result<&CudaSlice<T>> {
+        T::as_cuda_slice(self)
+    }
+}
+
 fn gemm_config<T>(
     alpha: T,
     beta: T,
diff --git a/candle-examples/Cargo.toml b/candle-examples/Cargo.toml
index 24435e81..f940a937 100644
--- a/candle-examples/Cargo.toml
+++ b/candle-examples/Cargo.toml
@@ -30,6 +30,9 @@ tracing-chrome = { workspace = true }
 tracing-subscriber = { workspace = true }
 wav = { workspace = true }
 
+[build-dependencies]
+anyhow = { workspace = true }
+
 [features]
 default = []
 cuda = ["candle/cuda", "candle-nn/cuda", "candle-transformers/cuda"]
diff --git a/candle-examples/build.rs b/candle-examples/build.rs
new file mode 100644
index 00000000..7f69fa77
--- /dev/null
+++ b/candle-examples/build.rs
@@ -0,0 +1,231 @@
+#![allow(unused)]
+use anyhow::{Context, Result};
+use std::io::Write;
+use std::path::PathBuf;
+
+struct KernelDirectories {
+    kernel_dir: &'static str,
+    rust_target: &'static str,
+}
+
+const DIRS: [KernelDirectories; 1] = [KernelDirectories {
+    kernel_dir: "examples/custom-ops/kernels/",
+    rust_target: "examples/custom-ops/cuda_kernels.rs",
+}];
+
+impl KernelDirectories {
+    fn maybe_build_ptx(
+        &self,
+        cu_file: &std::path::Path,
+        ptx_file: &std::path::Path,
+        compute_cap: usize,
+    ) -> Result<()> {
+        let should_compile = if ptx_file.exists() {
+            let ptx_modified = ptx_file.metadata()?.modified()?;
+            let cu_modified = cu_file.metadata()?.modified()?;
+            cu_modified.duration_since(ptx_modified).is_ok()
+        } else {
+            true
+        };
+        if should_compile {
+            #[cfg(feature = "cuda")]
+            {
+                let mut command = std::process::Command::new("nvcc");
+                let out_dir = ptx_file.parent().context("no parent for ptx file")?;
+                command
+                    .arg(format!("--gpu-architecture=sm_{compute_cap}"))
+                    .arg("--ptx")
+                    .args(["--default-stream", "per-thread"])
+                    .args(["--output-directory", out_dir.to_str().unwrap()])
+                    .arg(format!("-I/{}", self.kernel_dir))
+                    .arg(cu_file);
+                let output = command
+                    .spawn()
+                    .context("failed spawning nvcc")?
+                    .wait_with_output()?;
+                if !output.status.success() {
+                    anyhow::bail!(
+                    "nvcc error while compiling {cu_file:?}:\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
+                    String::from_utf8_lossy(&output.stdout),
+                    String::from_utf8_lossy(&output.stderr)
+                )
+                }
+            }
+            #[cfg(not(feature = "cuda"))]
+            std::fs::OpenOptions::new()
+                .create(true)
+                .write(true)
+                .open(ptx_file)?;
+        }
+        Ok(())
+    }
+    fn process(&self, out_dir: &std::path::Path, compute_cap: usize) -> Result<()> {
+        println!("cargo:rerun-if-changed={}", self.kernel_dir);
+        let kernel_dir = PathBuf::from(self.kernel_dir);
+        let out_dir = out_dir.join(self.kernel_dir);
+        if !out_dir.exists() {
+            std::fs::create_dir_all(&out_dir)?;
+        }
+        let mut cu_files = vec![];
+        let mut cuh_files = vec![];
+        for file in std::fs::read_dir(kernel_dir)?.flatten() {
+            let file = file.path();
+            match file.extension().and_then(|v| v.to_str()) {
+                Some("cu") => cu_files.push(file),
+                Some("cuh") => cuh_files.push(file),
+                _ => {}
+            }
+        }
+
+        let mut ptx_paths = vec![];
+        for cu_file in cu_files.iter() {
+            let file_stem = cu_file
+                .file_stem()
+                .with_context(|| format!("no stem {cu_file:?}"))?;
+            let file_stem = file_stem.to_string_lossy().into_owned();
+            let ptx_file = out_dir.join(&format!("{file_stem}.ptx"));
+            self.maybe_build_ptx(cu_file, &ptx_file, compute_cap)?;
+            ptx_paths.push(ptx_file);
+        }
+
+        let regenerate_rs_file = true;
+        if regenerate_rs_file {
+            let mut file = std::fs::File::create(self.rust_target)?;
+            for ptx_path in ptx_paths {
+                let name = ptx_path
+                    .file_stem()
+                    .context("empty stem")?
+                    .to_string_lossy();
+                let const_definition = format!(
+                    r#"pub const {}: &str = include_str!(concat!(env!("OUT_DIR"), "/{}/{name}.ptx"));"#,
+                    name.to_uppercase().replace('.', "_"),
+                    self.kernel_dir,
+                );
+                file.write_all(const_definition.as_bytes())?;
+                file.write_all(b"\n")?;
+            }
+        }
+        Ok(())
+    }
+}
+
+fn main() -> Result<()> {
+    println!("cargo:rerun-if-changed=build.rs");
+
+    let out_dir = std::env::var("OUT_DIR").context("OUT_DIR not set")?;
+    let out_dir = PathBuf::from(out_dir);
+    #[cfg(feature = "cuda")]
+    set_cuda_include_dir()?;
+    #[cfg(feature = "cuda")]
+    let compute_cap = compute_cap()?;
+    #[cfg(not(feature = "cuda"))]
+    let compute_cap = 0;
+    for d in DIRS {
+        d.process(&out_dir, compute_cap)?
+    }
+    Ok(())
+}
+
+fn set_cuda_include_dir() -> Result<()> {
+    // NOTE: copied from cudarc build.rs.
+    let env_vars = [
+        "CUDA_PATH",
+        "CUDA_ROOT",
+        "CUDA_TOOLKIT_ROOT_DIR",
+        "CUDNN_LIB",
+    ];
+    let env_vars = env_vars
+        .into_iter()
+        .map(std::env::var)
+        .filter_map(Result::ok)
+        .map(Into::<PathBuf>::into);
+
+    let roots = [
+        "/usr",
+        "/usr/local/cuda",
+        "/opt/cuda",
+        "/usr/lib/cuda",
+        "C:/Program Files/NVIDIA GPU Computing Toolkit",
+        "C:/CUDA",
+    ];
+    let roots = roots.into_iter().map(Into::<PathBuf>::into);
+    let root = env_vars
+        .chain(roots)
+        .find(|path| path.join("include").join("cuda.h").is_file())
+        .context("cannot find include/cuda.h")?;
+    println!(
+        "cargo:rustc-env=CUDA_INCLUDE_DIR={}",
+        root.join("include").display()
+    );
+    Ok(())
+}
+
+#[allow(unused)]
+fn compute_cap() -> Result<usize> {
+    // Grab compute code from nvidia-smi
+    let mut compute_cap = {
+        let out = std::process::Command::new("nvidia-smi")
+                    .arg("--query-gpu=compute_cap")
+                    .arg("--format=csv")
+                    .output()
+                    .context("`nvidia-smi` failed. Ensure that you have CUDA installed and that `nvidia-smi` is in your PATH.")?;
+        let out = std::str::from_utf8(&out.stdout).context("stdout is not a utf8 string")?;
+        let mut lines = out.lines();
+        assert_eq!(
+            lines.next().context("missing line in stdout")?,
+            "compute_cap"
+        );
+        let cap = lines
+            .next()
+            .context("missing line in stdout")?
+            .replace('.', "");
+        cap.parse::<usize>()
+            .with_context(|| format!("cannot parse as int {cap}"))?
+    };
+
+    // Grab available GPU codes from nvcc and select the highest one
+    let max_nvcc_code = {
+        let out = std::process::Command::new("nvcc")
+                    .arg("--list-gpu-code")
+                    .output()
+                    .expect("`nvcc` failed. Ensure that you have CUDA installed and that `nvcc` is in your PATH.");
+        let out = std::str::from_utf8(&out.stdout).unwrap();
+
+        let out = out.lines().collect::<Vec<&str>>();
+        let mut codes = Vec::with_capacity(out.len());
+        for code in out {
+            let code = code.split('_').collect::<Vec<&str>>();
+            if !code.is_empty() && code.contains(&"sm") {
+                if let Ok(num) = code[1].parse::<usize>() {
+                    codes.push(num);
+                }
+            }
+        }
+        codes.sort();
+        if !codes.contains(&compute_cap) {
+            anyhow::bail!(
+                "nvcc cannot target gpu arch {compute_cap}. Available nvcc targets are {codes:?}."
+            );
+        }
+        *codes.last().unwrap()
+    };
+
+    // If nvidia-smi compute_cap is higher than the highest gpu code from nvcc,
+    // then choose the highest gpu code in nvcc
+    if compute_cap > max_nvcc_code {
+        println!(
+            "cargo:warning=Lowering gpu arch {compute_cap} to max nvcc target {max_nvcc_code}."
+        );
+        compute_cap = max_nvcc_code;
+    }
+
+    println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP");
+    if let Ok(compute_cap_str) = std::env::var("CUDA_COMPUTE_CAP") {
+        compute_cap = compute_cap_str
+            .parse::<usize>()
+            .with_context(|| format!("cannot parse as usize '{compute_cap_str}'"))?;
+        println!("cargo:warning=Using gpu arch {compute_cap} from $CUDA_COMPUTE_CAP");
+    }
+    println!("cargo:rustc-env=CUDA_COMPUTE_CAP=sm_{compute_cap}");
+    Ok(compute_cap)
+}
diff --git a/candle-examples/examples/custom-ops/cuda_kernels.rs b/candle-examples/examples/custom-ops/cuda_kernels.rs
new file mode 100644
index 00000000..07d18342
--- /dev/null
+++ b/candle-examples/examples/custom-ops/cuda_kernels.rs
@@ -0,0 +1 @@
+pub const LAYERNORM_KERNELS: &str = include_str!(concat!(env!("OUT_DIR"), "/examples/custom-ops/kernels//layernorm_kernels.ptx"));
diff --git a/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu b/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu
new file mode 100644
index 00000000..07ab8639
--- /dev/null
+++ b/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu
@@ -0,0 +1,37 @@
+#include "reduction_utils.cuh"
+
+template <typename scalar_t>
+__device__ void
+rms_norm_kernel(scalar_t *__restrict__ out,         // [num_tokens, hidden_size]
+                const scalar_t *__restrict__ input, // [num_tokens, hidden_size]
+                const scalar_t *__restrict__ weight, // [hidden_size]
+                const float epsilon, const int num_tokens,
+                const int hidden_size) {
+  __shared__ float s_variance;
+  float variance = 0.0f;
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    const float x = (float)input[blockIdx.x * hidden_size + idx];
+    variance += x * x;
+  }
+  variance = blockReduceSum<float>(variance);
+  if (threadIdx.x == 0) {
+    s_variance = rsqrtf(variance / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    out[blockIdx.x * hidden_size + idx] =
+        ((scalar_t)(x * s_variance)) * weight[idx];
+  }
+}
+extern "C" __global__ void rms_norm_kernel_f32(
+    float *__restrict__ out,         // [num_tokens, hidden_size]
+    const float *__restrict__ input, // [num_tokens, hidden_size]
+    const float *__restrict__ weight, // [hidden_size]
+    const float epsilon, const int num_tokens,
+    const int hidden_size) {
+  rms_norm_kernel(out, input, weight, epsilon, num_tokens, hidden_size);
+}
+
diff --git a/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh b/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh
new file mode 100644
index 00000000..d5765f4f
--- /dev/null
+++ b/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh
@@ -0,0 +1,46 @@
+/*
+ * Adapted from
+ * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/reduce_kernel_utils.cuh
+ * Copyright (c) 2023, The vLLM team.
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+template <typename T> __inline__ __device__ T warpReduceSum(T val) {
+#pragma unroll
+  for (int mask = 16; mask > 0; mask >>= 1)
+    val += __shfl_xor_sync(0xffffffff, val, mask, 32);
+  return val;
+}
+
+/* Calculate the sum of all elements in a block */
+template <typename T> __inline__ __device__ T blockReduceSum(T val) {
+  static __shared__ T shared[32];
+  int lane = threadIdx.x & 0x1f;
+  int wid = threadIdx.x >> 5;
+
+  val = warpReduceSum<T>(val);
+
+  if (lane == 0)
+    shared[wid] = val;
+
+  __syncthreads();
+
+  // Modify from blockDim.x << 5 to blockDim.x / 32. to prevent
+  // blockDim.x is not divided by 32
+  val = (threadIdx.x < (blockDim.x / 32.f)) ? shared[lane] : (T)(0.0f);
+  val = warpReduceSum<T>(val);
+  return val;
+}
diff --git a/candle-examples/examples/custom-ops/main.rs b/candle-examples/examples/custom-ops/main.rs
new file mode 100644
index 00000000..adc7abd7
--- /dev/null
+++ b/candle-examples/examples/custom-ops/main.rs
@@ -0,0 +1,65 @@
+#![allow(dead_code)]
+#![allow(unused)]
+
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use clap::Parser;
+
+use candle::backend::BackendStorage;
+use candle::cpu_backend;
+use candle::{CpuStorage, CustomOp1, DType, Device, Error, Layout, Result, Shape, Tensor};
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+}
+
+struct LayerNorm;
+
+impl CustomOp1 for LayerNorm {
+    fn name(&self) -> &'static str {
+        "layer-norm"
+    }
+
+    fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> {
+        let s = s.as_slice::<f32>()?;
+        let _s = match l.contiguous_offsets() {
+            None => Err(Error::Wrapped("input has to be contiguous".into()))?,
+            Some((o1, o2)) => &s[o1..o2],
+        };
+        todo!()
+    }
+
+    #[cfg(feature = "cuda")]
+    fn cuda_fwd(
+        &self,
+        s: &candle::CudaStorage,
+        l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        let device = s.device().clone();
+        let s = s.as_cuda_slice::<f32>()?;
+        let s = match l.contiguous_offsets() {
+            None => Err(Error::Wrapped("input has to be contiguous".into()))?,
+            Some((o1, o2)) => s, // TODO: slice with o1 and o2
+        };
+        let s: std::result::Result<_, candle::cuda_backend::CudaError> =
+            s.try_clone().map_err(|v| v.into());
+        let s = s?;
+        let s = candle::CudaStorage::wrap_cuda_slice(s, device);
+        Ok((s, l.shape().clone()))
+    }
+}
+
+fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+    let device = candle_examples::device(args.cpu)?;
+    let t = Tensor::arange(0f32, 14f32, &device)?.reshape((2, 7))?;
+    println!("{t}");
+    let t = t.custom_op1(LayerNorm)?;
+    println!("{t}");
+    Ok(())
+}